FusedMultiHeadAttention

class paddle.incubate.nn. FusedMultiHeadAttention ( embed_dim, num_heads, dropout_rate=0.5, attn_dropout_rate=0.5, kdim=None, vdim=None, normalize_before=False, need_weights=False, weight_attr=None, bias_attr=None, name=None ) [source]

Attention mapps queries and a set of key-value pairs to outputs, and Multi-Head Attention performs multiple parallel attention to jointly attending to information from different representation subspaces. Please refer to Attention Is All You Need for more details.

Parameters
  • embed_dim (int) – The expected feature size in the input and output.

  • num_heads (int) – The number of heads in multi-head attention.

  • dropout_rate (float, optional) – The dropout probability used on attention weights to drop some attention targets for the dropout after attention. 0 for no dropout. Default 0.5.

  • attn_dropout_rate (float, optional) – The dropout probability used on attention weights to drop some attention targets for the dropout in attention. 0 for no dropout. Default 0.5.

  • kdim (int, optional) – The feature size in key. If None, assumed equal to embed_dim. Default None.

  • vdim (int, optional) – The feature size in value. If None, assumed equal to embed_dim. Default None.

  • normalize_before (bool, optional) – Indicate whether it is pre_layer_norm (True) or post_layer_norm architecture (False). Default False.

  • need_weights (bool, optional) – Indicate whether to return the attention weights. Now, only False is supported. Default False.

  • weight_attr (ParamAttr, optional) – To specify the weight parameter property. Default: None, which means the default weight parameter property is used. See usage for details in ParamAttr.

  • bias_attr (ParamAttr|bool, optional) – To specify the bias parameter property. Default: None, which means the default bias parameter property is used. If it is set to False, this layer will not have trainable bias parameter. See usage for details in ParamAttr.

Examples

# required: gpu
import paddle
# input: [batch_size, sequence_length, embed_dim]
query = paddle.rand((2, 4, 128))
# self attention mask: [batch_size, num_heads, query_len, query_len]
attn_mask = paddle.rand((2, 2, 4, 4))
multi_head_attn = paddle.incubate.nn.FusedMultiHeadAttention(128, 2)
output = multi_head_attn(query, None, None, attn_mask=attn_mask)  # [2, 4, 128]
forward ( query, key=None, value=None, attn_mask=None, cache=None )

forward

Applies multi-head attention to map queries and a set of key-value pairs to outputs.

Parameters
  • query (Tensor) – The queries for multi-head attention. It is a tensor with shape [batch_size, query_length, embed_dim]. The data type should be float32 or float64.

  • key (Tensor, optional) – The keys for multi-head attention. It is a tensor with shape [batch_size, key_length, kdim]. The data type should be float32 or float64. If None, use query as key. Default None.

  • value (Tensor, optional) – The values for multi-head attention. It is a tensor with shape [batch_size, value_length, vdim]. The data type should be float32 or float64. If None, use query as value. Default None.

  • attn_mask (Tensor, optional) – A tensor used in multi-head attention to prevents attention to some unwanted positions, usually the paddings or the subsequent positions. It is a tensor with shape broadcasted to [batch_size, n_head, sequence_length, sequence_length]. When the data type is bool, the unwanted positions have False values and the others have True values. When the data type is int, the unwanted positions have 0 values and the others have 1 values. When the data type is float, the unwanted positions have -INF values and the others have 0 values. It can be None when nothing wanted or needed to be prevented attention to. Default None.

  • cache (MultiHeadAttention.Cache|MultiHeadAttention.StaticCache, optional) – Now, only None is supported. Default None.

Returns

It is a tensor that has the same shape and data type as query, representing attention output.

Return type

Tensor|tuple